In a wireless communication network, data is transmitted wirelessly from a transmitter to a receiver. For example, in the Wireless Local Area Network technology, WLAN, data is transmitted from a wireless access point to a wireless communication device, called downlink communication, or from the wireless communication device to the wireless access point, called uplink communication. Data may also be transmitted peer-to-peer, i.e. directly between wireless communication devices. The data that is transmitted may be coded with different modulation and coding schemes, MCS, depending on the current communication conditions, such as signal to noise ratio, SNR. In good communication conditions, e.g. high SNR, a more advanced MCS may be used than in communication conditions having lower SNR. For example with a 64 Quadrature Amplitude Modulation, QAM, scheme, 6 data bits may be transmitted in one QAM symbol whereas for 16QAM only 4 information bits may be transmitted in one QAM symbol.
There is a need to find the most suitable MCS for wireless communication between a transmitter and a receiver, depending on the communication conditions. This may also be called link adaptation, i.e. to adapt the wireless link between the transmitter and the receiver.
There are different known methods for link adaptation. One such method is based on the Minstrel algorithm. The Minstrel algorithm is described on the following Internet homepage: http://linuxwireless.org/en/developers/Documentation/mac80211/RateControl/minstrel. The minstrel algorithm keeps in a memory a table of MCS and throughput statistics for individual receivers in the network, and uses this table to choose the MCS that historically has provided the best throughput for the individual receiver. However, in order to update the table, other MCSs than the best are also sampled and used. This algorithm works well when there is lots of data being transmitted, e.g. more than hundreds of packets, and when the channel conditions are static, or slowly varying. With the growing interest in Internet of Things, one focus is on communication devices that are sensor stations, STA. A sensor STA may be sleeping for long periods of time, e.g. up to years, wake up, transmit some data and going back to sleep. Sensor STA's are typically battery powered and therefore choosing the correct MCS as soon as possible is important, not to waste battery power. Since such long time may have passed since the sensor STA was waken last time, the channel conditions may be completely different. Since the Minstrel algorithm is based on statistics attained through many packets, this algorithm is insufficient for such sensor STAs. An example of the convergence of the Minstrel algorithm is shown in FIG. 1. As shown, a lot of data samples are needed before the algorithm converges to a correct data, in this example correct throughput. Consequently, the minstrel algorithm is not feasible for wireless devices that wake up seldom. Other examples of interest where the Minstrel algorithm may fail can be in vehicle to vehicle or vehicle to infrastructure (V2X) communication. Here the communication devices may or may not be asleep, but certainly the environment may change rapidly.
In the WLAN standards 802.11n and future standards, there is a possibility for a receiver to suggest an MCS to the transmitter using an MCS feedback field in the HT Control field. This is described in the IEEE standard 802.11—2012, “Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications”, section 9.28.2 Using the MCS feedback, the challenge is for the receiver to estimate which MCS to suggest for the feedback. One alternative is to estimate the SNR of a previously transmitted packet and use this SNR estimate to choose the right MCS. However, the problem with this approach is firstly that estimating SNR accurately is not trivial; and secondly, the SNR does not give the full picture of the channel conditions. There are other alternatives to estimate the MCS based on link quality, for example using mutual information or Received Signal Strength Indication, RSSI, but they all suffer from the challenge of providing accurate estimates. An MCS estimate based on SNR and/or channel estimates is in fact dependent on the MCS used for the signal on which the SNR or channel estimate was made. This is due to the Error Vector Magnitude, EVM, requirements, which vary with the MCS. For example, suppose that a received packet is Binary phase-shift keying, BPSK, modulated, and the estimated SNR based on this packet is 20 dB. However, had the packet been 64QAM modulated, assuming same propagation environment, the estimated SNR would have been lower, say 12 dB. In this example the transmitter must back off 10 dB in order to fulfill the EVM requirements for 64 QAM. Note also that the difference in SNR cannot be explained only by the difference in TX powers, as higher output powers usually introduce non-linear distortions in the transmitted signal.
Consequently, there is a need for a better way for selecting MCS to use for wireless communication between a transmitter and a receiver, especially to find a solution that works well for communication devices that are only seldom awake, such as sensor stations.